getF                  package:amer                  R Documentation

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_D_e_s_c_r_i_p_t_i_o_n:

     get the estimated function values from an amer-Fit

_U_s_a_g_e:

     getF(object, which, n=100, newdata, interval=c("NONE", "MCMC",
         "RW"), addConst=TRUE, varying=1, level=0.9, sims=1000)

_A_r_g_u_m_e_n_t_s:

  object: a fitted additive (mixed) model of class 'amer-class'

   which: (optional) an integer vector or a character vector of names
          giving the smooths for which fitted values are desired.
          Defaults to all.

       n: if no 'newdata' is given, fitted values for a regular grid
          with n values in the range of the respective covariates are
          returned

 newdata: An optional data frame in which to look for variables with
          which to predict

interval: what mehod should be used to compute pointwise confidence/HPD
          intervals: RW= bias-adjusted empirical bayes, MCMC uses
          'mcmcsamp'

addConst: boolean should the global intercept and intercepts for the
          levels of the by-variable be included in the fitted values
          (and their CIs) can also be a vector of the same length as
          'which'

 varying: value of the'varying'-covariate (see 'tp') to be used if no
          newdata is supplied.  Defaults to 1.

   level: level for the confidence/HPD intervals

    sims: how many iterates should  be generated for the MCMC-based
          HPD-intervals

_V_a_l_u_e:

     a list with one 'data.frame' for each function, giving 'newdata'
     or the values of the generated grid plus the fitted values (and
     confidence/HPD intervals) if MCMC-intervals were rquested, the
     listhas an attribute "mcmc" containing the result of the call to
     'mcmcsamp', a 'merMCMC-class' object.

_N_o_t_e:

     The formula used for the pointwise bias-adjusted CIs is taken from
     Ruppert and Wand's  'Semiparametric Regression' (2003), p. 140. 
     These leave out the uncertainty associated with the variance
     component estimates.  MCMC-intervals based on results from
     'mcmcsamp' don't seem to be very reliable yet and should be used
     with caution, especially for more complex models.

_A_u_t_h_o_r(_s):

     Fabian Scheipl

_S_e_e _A_l_s_o:

     'plotF',  'tests/optionsTests.r' and the vignette for examples

